AI Platform Engineer
Tasks
- Build and maintain MLOps automation
- Build and operate fine tuning pipelines
- Build and operate inference request router
- Build and operate production AI inference runtimes
- Build fault tolerant distributed training infrastructure
- Create regression testing and benchmarking
- Design and optimize distributed KV cache systems
- Develop large-scale distributed training pipelines
- Host and manage multi LoRA adapters
- Implement distributed inference optimizations
- Implement inference optimizations
- Profile and resolve training bottlenecks
Perks/Benefits
- N/A
Skills/Tech-stack
Automated Evaluation | CI/CD | CUDA | Continuous Checkpointing | Continuous batching | DCGM | DPO | DeepSpeed | Distributed KV Cache | Experiment tracking | FSDP | Fault Tolerance | FlashAttention | Golang | KV cache | Kubernetes | MLOps | Megatron-LM | Model Registry | Model versioning | Multi-LoRa | NCCL | NVIDIA Triton | Nsight Systems | PagedAttention | Prefill Decode | Prefill decode disaggregation | PyTorch | PyTorch Profiler | Python | Quantization | RLHF | Request Routing | SFT | SGLang | Speculative decoding | TensorRT-LLM | Triton | VLLM
Roles
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